论文标题
深度学习的创造力：概念化和评估
Creativity of Deep Learning: Conceptualization and Assessment
论文作者
论文摘要
尽管已经很好地探索了深度学习（DL）自动化简单任务的潜力，但最近的研究已经开始调查深度学习用于创意设计的使用，既可以完整的人工制品创建和支持人类在创建过程中的支持。在本文中，我们使用从计算创造力的见解来概念化和评估文献综述中确定的创意领域中生成深度学习的当前应用。我们重点介绍了当前系统与人类创造力的不同模型及其缺点之间的相似之处。虽然深度学习产生了高价值的结果，例如高质量图像，但由于多种原因，例如与训练数据定义的概念空间相关联，它们的新颖性通常受到限制。当前的DL方法也不允许改变内部问题表示形式，并且缺乏识别高度不同领域的联系的能力，这两者都被视为人类创造力的主要驱动力。
While the potential of deep learning (DL) for automating simple tasks is already well explored, recent research has started investigating the use of deep learning for creative design, both for complete artifact creation and supporting humans in the creation process. In this paper, we use insights from computational creativity to conceptualize and assess current applications of generative deep learning in creative domains identified in a literature review. We highlight parallels between current systems and different models of human creativity as well as their shortcomings. While deep learning yields results of high value, such as high-quality images, their novelty is typically limited due to multiple reasons such as being tied to a conceptual space defined by training data. Current DL methods also do not allow for changes in the internal problem representation, and they lack the capability to identify connections across highly different domains, both of which are seen as major drivers of human creativity.
